Content item impression effect decay

ABSTRACT

When a content item is initially served to a client device, the content item may result in an impression effect. As time elapses, the initial impression may fade. Such a decay of the impression effect may be predicted through the use of a predictive model. In some implementations, one or more impression effect parameters may be accessed and used with the predictive model to determine a decay factor or predicted value that incorporates the impression effect decay for a content item. A value, such as a score, may be determined based on the decay factor or the predicted value and a bid associated with a content item. A content item may be selected based on the determined value and data to effect presentation of the content item may be provided.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/872,412, entitled “Content Item Impression Effect Decay,” filed Aug.30, 2013, the disclosure of which is incorporated by reference herein inits entirety.

BACKGROUND

In a networked environment, such as the Internet or other networks,first-party content providers can provide information for publicpresentation on resources, for example webpages, documents,applications, and/or other resources. The first-party content caninclude text, video, and/or audio information provided by thefirst-party content providers via, for example, a resource server forpresentation on a client device over the Internet. The first-partycontent may be a webpage requested by the client device or a stand-aloneapplication (e.g., a video game, a chat program, etc.) running on theclient device. Additional third-party content can also be provided bythird-party content providers for presentation on the client devicetogether with the first-party content provided by the first-partycontent providers. For example, the third-party content may be a publicservice announcement or advertisement that appears in conjunction with arequested resource, such as a webpage (e.g., a search result webpagefrom a search engine, a webpage that includes an online article, awebpage of a social networking service, etc.) or with an application(e.g., an advertisement within a game). Thus, a person viewing aresource can access the first-party content that is the subject of theresource as well as the third-party content that may or may not berelated to the subject matter of the resource.

SUMMARY

Implementations described herein relate to modeling an impression effectand the decay thereof. The modeling of the impression effect and thedecay may be used in determining whether to select a content item, suchas through the use of a predictive model. For example, after a contentitem, for example an advertisement, is served to be presented with aresource to a user, the content item may have an effect on the userviewing the content item, such as an impression effect. As time elapses,the effect of the impression fades as the user may forget about thecontent item. Such an impression effect may be used to initially reducethe likelihood that the content item is selected and served soon afteran initial impression, but, as time elapses or decays, the impressioneffect may reduce such that the likelihood of the content item beingselected and served increases.

One implementation relates to a method of selecting and serving acontent item based on a decay of an impression effect for the contentitem. The method includes accessing one or more impression effectparameters stored in a data structure. A decay factor of an impressioneffect for a content item is determined using the impression effectdecay predictive model and the accessed one or more impression effectparameters. A value for the content item may be determined based, atleast in part, on the decay factor and a bid associated with the contentitem. The content item may be selected based, at least in part, on thedetermined value. Data to effect presentation of the selected contentitem may be provided.

Another implementation relates to a system for serving content items incontent item slots. The system may include one or more processingmodules and one or more storage devices. The one or more storage devicesincludes instructions that cause the one or more processing modules toperform several operations. The operations include accessing priorimpression effect parameters stored in a data structure. The priorimpression effect parameters may be associated with a client device, aresource, or a content item. The operations may include generating animpression effect decay predictive model based, at least in part, on theprior impression effect parameters. One or more current impressioneffect parameters may be received and a decay factor of an impressioneffect may be determined for a content item using the impression effectdecay predictive model and the received one or more current impressioneffect parameters. The operations may further include determining avalue for the content item based, at least in part, on the decay factorand a bid associated with the content item. The content item may beselected based, at least in part on the determined value and data toeffect presentation of the selected content item may be provided.

Yet a further implementation relates to a computer readable storagedevice storing instructions that, when executed by one or moreprocessing modules, cause the one or more processing modules to performseveral operations. The operations may include receiving one or moreimpression effect parameters and a predictive model. The operations mayfurther include determining a predicted value for a content item usingthe predictive model and the received one or more impression effectparameters. A value for the content item may be determined based, atleast in part, on the predicted value and a bid associated with thecontent item. The operations may further include selecting the contentitem based, at least in part, on the determined value and providing datato effect presentation of the selected content item.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,aspects, and advantages of the disclosure will become apparent from thedescription, the drawings, and the claims, in which:

FIG. 1 is an overview depicting an implementation of a system ofproviding information via a computer network;

FIG. 2 is an illustration of an implementation of a first-party resourcehaving third-party content;

FIG. 3 is an illustration of an implementation of another first-partyresource displayed on a mobile device and having third-party content;

FIG. 4 is a graphical diagram depicting an impression effect decayingover a time period;

FIG. 5 is a graphical diagram depicting another impression effectdecaying over a time period;

FIG. 6 is a block diagram of an implementation of a system for selectingcontent items using a predictive model;

FIG. 7 is a flow diagram of an implementation of a process for traininga predictive model;

FIG. 8 is a flow diagram of an implementation of a process for using apredictive model to select a content item;

FIG. 9 is a flow diagram of another implementation of a process forusing a predictive model to select a content item;

FIG. 10 is a flow diagram of yet another implementation of a process forusing a predictive model to select a content item; and

FIG. 11 is a block diagram depicting a general architecture for acomputer system that may be employed to implement various elements ofthe systems and methods described and illustrated herein.

It will be recognized that some or all of the figures are schematicrepresentations for purposes of illustration. The figures are providedfor the purpose of illustrating one or more embodiments with theexplicit understanding that they will not be used to limit the scope orthe meaning of the claims.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and implementations of, methods, apparatuses, and systemsfor providing information on a computer network. The various conceptsintroduced above and discussed in greater detail below may beimplemented in any of numerous ways as the described concepts are notlimited to any particular manner of implementation. Examples of specificimplementations and applications are provided primarily for illustrativepurposes.

A computing device (e.g., a client device) can view a resource, such asa webpage, a document, an application, etc. In some implementations, thecomputing device may access the resource via the Internet bycommunicating with a server, such as a webpage server, corresponding tothat resource. The resource includes first-party content that is thesubject of the resource from a first-party content provider and may alsoinclude additional third-party provided content, such as advertisementsor other content. In one implementation, responsive to receiving arequest to access a webpage, a webpage server and/or a client device cancommunicate with a data processing system, such as a content itemselection system, to request a content item to be presented with therequested webpage, such as through the execution of code of the resourceto request a third-party content item to be presented with the resource.The content item selection system can select a third-party content itemand provide data to effect presentation of the content item with therequested webpage on a display of the client device. In some instances,the content item is selected and served with a resource associated witha search query response. For example, a search engine may return searchresults on a search results webpage and may include third-party contentitems related to the search query in one or more content item slots ofthe search results webpage.

The computing device (e.g., a client device) may also be used to view orexecute an application, such as a mobile application. The applicationmay include first-party content that is the subject of the applicationfrom a first-party content provider and may also include additionalthird-party provided content, such as advertisements or other content.In one implementation, responsive to use of the application, a resourceserver and/or a client device can communicate with a data processingsystem, such as a content item selection system, to request a contentitem to be presented with a user interface of the application and/orotherwise. The content item selection system can select a third-partycontent item and provide data to effect presentation of the content itemwith the application on a display of the client device.

In some instances, a device identifier may be associated with the clientdevice. The device identifier may be a randomized number associated withthe client device to identify the device during subsequent requests forresources and/or content items. In some instances, the device identifiermay be configured to store and/or cause the client device to transmitinformation related to the client device to the content item selectionsystem and/or resource server (e.g., values of sensor data, a webbrowser type, an operating system, historical resource requests,historical content item requests, etc.).

In situations in which the systems discussed here collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, social actions or activities, profession, a user'spreferences, or a user's current location), or to control whether and/orhow to receive content from the content server that may be more relevantto the user. In addition, certain data may be treated in one or moreways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be treated sothat no personally identifiable information can be determined for theuser, or a user's geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may have control over how information is collected about the userand used by a content server.

A third-party content provider, when providing third-party content itemsfor presentation with requested resources via the Internet or othernetwork, may utilize a content item management service to control orotherwise influence the selection and serving of the third-party contentitems. For instance, a third-party content provider may specifyselection criteria (such as keywords) and corresponding bid values thatare used in the selection of the third-party content items. The bidvalues may be utilized by the content item selection system in anauction to select and serve content items for presentation with aresource. For example, a third-party content provider may place a bid inthe auction that corresponds to an agreement to pay a certain amount ofmoney if a user interacts with the provider's content item (e.g., theprovider agrees to pay $3 if a user clicks on the provider's contentitem). In other examples, a third-party content provider may place a bidin the auction that corresponds to an agreement to pay a certain amountof money if the content item is selected and served (e.g., the provideragrees to pay $0.005 each time a content item is selected and served).In some instances, the content item selection system uses content iteminteraction data to determine the performance of the third-party contentprovider's content items. For example, users may be more inclined toclick on third-party content items on certain webpages over others.Accordingly, auction bids to place the third-party content items may behigher for high-performing webpages, categories of webpages, and/orother criteria, while the bids may be lower for low-performing webpages,categories of webpages, and/or other criteria.

In some instances, one or more performance metrics for the third-partycontent items may be determined and indications of such performancemetrics may be provided to the third-party content provider via a userinterface for the content item management account. For example, theperformance metrics may include a cost per impression (CPI) or cost perthousand impressions (CPM), where an impression may be counted, forexample, whenever a content item is selected to be served forpresentation with a resource. In some instances, the performance metricmay include a click-through rate (CTR), defined as the number of clickson the content item divided by the number of impressions. Still otherperformance metrics, such as cost per action (CPA) (where an action maybe clicking on the content item or a link therein, a purchase of aproduct, a referral of the content item, etc.), conversion rate (CVR),cost per click-through (CPC) (counted when a content item is clicked),cost per sale (CPS), cost per lead (CPL), effective CPM (eCPM), and/orother performance metrics may be used.

In some instances, a webpage or other resource (such as, for example, anapplication) includes one or more content item slots in which a selectedand served third-party content item may be displayed. The code (e.g.,JavaScript®, HTML, etc.) defining a content item slot for a webpage orother resource may include instructions to request a third-party contentitem from the content item selection system to be presented with thewebpage. In some implementations, the code may include an image requesthaving a content item request URL (e.g.,/page/contentitem?devid=abc123&devnfo=A34r0). The content item requestURL may include one or more parameters. Such parameters may, in someimplementations, be encoded strings such as “devid=abc123” and/or“devnfo=A34r0.”

The selection of a third-party content item to be served with theresource by a content item selection system may be based on severalinfluencing factors, such as a predicted click through rate (pCTR), apredicted conversion rate (pCVR), a bid associated with the contentitem, etc. Such influencing factors may be used to generate a value,such as a score, against which other scores for other content items maybe compared by the content item selection system through an auction.

During an auction for a content item slot for a resource, such as awebpage, several different types of bid values may be utilized bythird-party content providers for various third-party content items. Forexample, an auction may include bids based on whether a user clicks onthe third-party content item, whether a user performs a specific actionbased on the presentation of the third-party content item, whether thethird-party content item is selected and served, and/or other types ofbids. For example, a bid based on whether the third-party content itemis selected and served may be a lower bid (e.g., $0.005) while a bidbased on whether a user performs a specific action may be a higher bid(e.g., $5). In some instances, the bid may be adjusted to account for aprobability associated with the type of bid and/or adjusted for otherreasons. For example, the probability of the user performing thespecific action may be low, such as 0.2%, while the probability of theselected and served third-party content item may be 100% (e.g., theselected and served content item will occur if it is selected during theauction, so the bid is unadjusted). Accordingly, a value, such as ascore, may be generated to be used in the auction based on the bid valueand the probability or another modifying value. In the prior example,the value or score for a bid based on whether the third-party contentitem is selected and served may be $0.005*1.00=0.005 and the value orscore for a bid based on whether a user performs a specific action maybe $5*0.002=0.01. To maximize the income generated, the content itemselection system may select the third-party content item with thehighest value from the auction. In the foregoing example, the contentitem selection system may select the content item associated with thebid based on whether the user performs the specific action.

Once a third-party content item is selected by the content itemselection system, data to effect presentation of the third-party contentitem on a display of the client device may be provided to the clientdevice using a network.

After a content item, such as an advertisement, is served to bepresented with a resource to a user, the content item may have an effecton the user viewing the content item—an impression effect. As timeelapses, the effect of the impression fades as the user may forget aboutthe content item. In some implementations, the impression effect may belogged as a count of the times a content item is selected and served toa user within a given period, such as daily. Once the count exceeds apredetermined level, such as a frequency cap, the content item may beexcluded from future selection by the content item selection system.

In other implementations, the impression effect and decay thereof may beused to reduce the likelihood that the content item is selected andserved by the content item selection system soon after an initialimpression. As time elapses and the decay of the impression effectcontinues, it may be more useful to show the same content item to thesame user. Accordingly, the likelihood of the content item beingselected and served by the content item selection system may increasebased on the decaying impression effect in some implementations.

Thus, it may be useful to determine and/or model an impression effect ofa content item and the decay of the impression effect. Such modeling ofthe impression effect and the decay may be used by the content itemselection system during the determination of whether to select a contentitem. In some implementations, the modeling of the impression effect maybe over an extended period of time, such as the distribution of animpression effect over one week, two weeks, four weeks, etc. In someimplementations, the modeling may be effected through the use of apredictive model that outputs one or more values in response toreceiving one or more impression effect input parameters. In animplementation, the predictive model may output several values such thata graph, data table, or other data aggregation object may be generatedfor the predicted impression effect. In another implementation, thepredictive model may output several values such that a graph, datatable, or other data aggregation object may be generated for thepredicted decay of the impression effect.

The decay of the impression effect may be dependent on the user, thecontent item, the third-party content provider, a resource with whichthe content item is presented, etc. For example, each content item mayhave a different effect on retaining user interest, a different userdemand cycle time, etc. Thus, the decay of the impression effect mayvary for different content items. Similarly, different aggregate userprofiles may be associated with different interests, different behaviorpatterns, etc., and may react to repetitive impressions distinctively.Accordingly, one or more of the inputs for the predictive modelsdescribed herein may include parameters associated with an aggregateprofile, parameters associated with a client device, parametersassociated with a publisher or resource, parameters associated with acontent item or campaign, and/or parameters associated with athird-party content provider.

In other implementations, the predictive model may output a valuecorresponding to the current predicted impression effect and/or decayvalue. The predicted impression effect and/or decay value, for example adecay factor, may then be used in determining the value, such as ascore, by the content item selection system. That is, a predictedimpression effect and/or decay value for a given client device, a givencontent item, and a given resource and/or publisher may be used duringthe selection of a content item in response to a content item request.For example, an outputted decay factor from the predictive model may beused as a discount factor when determining the value. In still otherimplementations, the impression effect decay predictive model may beincorporated as part of a predictive model for other predicted values,such as a pCTR predictive model or a pCVR predictive model, such thatthe predictive model may take into account the impression effect decayin calculating the value, such as pCTR or pCVR.

The foregoing impression effect and decay thereof may be used to improvea user's engagement or experience with content items by improving theeffective delivery of relevant content items without oversaturating auser with relevant content items or unduly restricting a relevantcontent item from being selected and served to the user. Rather, theimpression effect and decay thereof described herein attempts to delivera relevant content item at the right time based on several impressioneffect parameters through the use of a predictive model. That is, animpression effect decay predictive model may learn the distribution oftime after an initial impression that it takes for a user to engage witha content item.

While the foregoing has provided an overview of modeling impressioneffect, the decay of the impression effect, and implementationsutilizing such modeling, more specific examples and systems to implementsuch a system will now be described.

FIG. 1 is a block diagram of an implementation of a system 100 forproviding information via at least one computer network such as thenetwork 106. The network 106 may include a local area network (LAN),wide area network (WAN), a telephone network, such as the PublicSwitched Telephone Network (PSTN), a wireless link, an intranet, theInternet, or combinations thereof. The system 100 can also include atleast one data processing system or processing module, such as a contentitem selection system 108. The content item selection system 108 caninclude at least one logic device, such as a computing device having adata processor, to communicate via the network 106, for example with aresource server 104, a client device 110, and/or a third-party contentserver 102. The content item selection system 108 can include one ormore data processors, such as a content placement processor, configuredto execute instructions stored in a memory device to perform one or moreoperations described herein. In other words, the one or more dataprocessors and the memory device of the content item selection system108 may form a processing module. The processor may include amicroprocessor, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), etc., or combinations thereof. Thememory may include, but is not limited to, electronic, optical,magnetic, or any other storage or transmission device capable ofproviding processor with program instructions. The memory may include afloppy disk, compact disc read-only memory (CD-ROM), digital versatiledisc (DVD), magnetic disk, memory chip, read-only memory (ROM),random-access memory (RAM), Electrically Erasable Programmable Read-OnlyMemory (EEPROM), erasable programmable read only memory (EPROM), flashmemory, optical media, or any other suitable memory from which processorcan read instructions. The instructions may include code from anysuitable computer programming language such as, but not limited to,ActionScript®, C, C++, C#, Java®, ActionScript®, JavaScript®, JSON,Perl®, HTML, HTML5, XML, Python®, and Visual Basic®. The processingmodule may process instructions and provide data to effect presentationof one or more content items to the resource server 104 and/or theclient device 110. In addition to the processing circuit, the contentitem selection system 108 may include one or more databases configuredto store data. The content item selection system 108 may also include aninterface configured to receive data via the network 106 and to providedata from the content item selection system 108 to any of the otherdevices on the network 106. The content item selection system 108 caninclude a server, such as an advertisement server or otherwise.

The client device 110 can include one or more devices such as acomputer, laptop, desktop, smart phone, tablet, personal digitalassistant, set-top box for a television set, a smart television, orserver device configured to communicate with other devices via thenetwork 106. The device may be any form of portable electronic devicethat includes a data processor and a memory, i.e., a processing module.The memory may store machine instructions that, when executed by aprocessor, cause the processor to perform one or more of the operationsdescribed herein. The memory may also store data to effect presentationof one or more resources, content items, etc. on the computing device.The processor may include a microprocessor, an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), etc.,or combinations thereof. The memory may include, but is not limited to,electronic, optical, magnetic, or any other storage or transmissiondevice capable of providing processor with program instructions. Thememory may include a floppy disk, compact disc read-only memory(CD-ROM), digital versatile disc (DVD), magnetic disk, memory chip,read-only memory (ROM), random-access memory (RAM), ElectricallyErasable Programmable Read-Only Memory (EEPROM), erasable programmableread only memory (EPROM), flash memory, optical media, or any othersuitable memory from which processor can read instructions. Theinstructions may include code from any suitable computer programminglanguage such as, but not limited to, ActionScript®, C, C++, C#, Java®,ActionScript®, JavaScript®, JSON, Perl®, HTML, HTML5, XML, Python®, andVisual Basic®.

The client device 110 can execute a software application (e.g., a webbrowser or other application) to retrieve content from other computingdevices over network 106. Such an application may be configured toretrieve first-party content from a resource server 104. In some cases,an application running on the client device 110 may itself befirst-party content (e.g., a game, a media player, etc.). In oneimplementation, the client device 110 may execute a web browserapplication which provides a browser window on a display of the clientdevice. The web browser application that provides the browser window mayoperate by receiving input of a uniform resource locator (URL), such asa web address, from an input device (e.g., a pointing device, akeyboard, a touch screen, or another form of input device). In response,one or more processors of the client device executing the instructionsfrom the web browser application may request data from another deviceconnected to the network 106 referred to by the URL address (e.g., aresource server 104). The other device may then provide webpage dataand/or other data to the client device 110, which causes visual indiciato be displayed by the display of the client device 110. Accordingly,the browser window displays the retrieved first-party content, such aswebpages from various websites, to facilitate user interaction with thefirst-party content.

The resource server 104 can include a computing device, such as aserver, configured to host a resource, such as a webpage or otherresource (e.g., articles, comment threads, music, video, graphics,search results, information feeds, etc.). The resource server 104 may bea computer server (e.g., a file transfer protocol (FTP) server, filesharing server, web server, etc.) or a combination of servers (e.g., adata center, a cloud computing platform, etc.). The resource server 104can provide resource data or other content (e.g., text documents, PDFfiles, and other forms of electronic documents) to the client device110. In one implementation, the client device 110 can access theresource server 104 via the network 106 to request data to effectpresentation of a resource of the resource server 104.

One or more third-party content providers may have third-party contentservers 102 to directly or indirectly provide data for third-partycontent items to the content item selection system 108 and/or to othercomputing devices via network 106. The content items may be in anyformat that may be presented on a display of a client device 110, forexample, graphical, text, image, audio, video, etc. The content itemsmay also be a combination (hybrid) of the formats. The content items maybe banner content items, interstitial content items, pop-up contentitems, rich media content items, hybrid content items, etc. The contentitems may also include embedded information such as hyperlinks,metadata, links, machine-executable instructions, annotations, etc. Insome instances, the third-party content servers 102 may be integratedinto the content item selection system 108 and/or the data for thethird-party content items may be stored in a database of the contentitem selection system 108.

In one implementation, the content item selection system 108 canreceive, via the network 106, a request for a content item to presentwith a resource. The received request may be received from a resourceserver 104, a client device 110, and/or any other computing device. Theresource server 104 may be owned or ran by a first-party contentprovider that may include instructions for the content item selectionsystem 108 to provide third-party content items with one or moreresources of the first-party content provider on the resource server104. In one implementation, the resource may include a webpage. Theclient device 110 may be a computing device operated by a user(represented by a device identifier), which, when accessing a resourceof the resource server 104, can make a request to the content itemselection system 108 for content items to be presented with theresource, for instance. The content item request can include requestingdevice information (e.g., a web browser type, an operating system type,one or more previous resource requests from the requesting device, oneor more previous content items received by the requesting device, alanguage setting for the requesting device, a geographical location ofthe requesting device, a time of a day at the requesting device, a dayof a week at the requesting device, a day of a month at the requestingdevice, a day of a year at the requesting device, etc.) and resourceinformation (e.g., URL of the requested resource, one or more keywordsof the content of the requested resource, text of the content of theresource, a title of the resource, a category of the resource, a type ofthe resource, a property of the resource, an interactivity level of theresource, a ranking of the resource, a popularity of the resource, acategory of a publisher associated with the resource, a type of apublisher associated with the resource, a property of a publisherassociated with the resource, etc.). The information or parameters thatthe content item selection system 108 receives can include a HyperTextTransfer Protocol (HTTP) cookie which contains a device identifier(e.g., a random number) that represents the client device 110. In someimplementations, the device and/or the resource information orparameters may be appended to a content item request URL (e.g.,/page/contentitem?devid=abc123&devnfo=A34r0). In some implementations,the device and/or the resource information or parameters may be encodedprior to being appended the content item request URL. The requestingdevice and/or the resource information or parameters may be utilized bythe content item selection system 108 to select third-party contentitems to be served with the requested resource and presented on adisplay of a client device 110.

In some instances, a resource of a resource server 104 may include asearch engine feature. The search engine feature may receive a searchquery (e.g., a string of text) via an input feature (an input text box,etc.). The search engine may search an index of documents (e.g., otherresources, such as webpages, etc.) for relevant search results based onthe search query. The search results may be transmitted as a secondresource to present the relevant search results, such as a search resultwebpage, on a display of a client device 110. The search results mayinclude webpage titles, hyperlinks, etc. One or more third-party contentitems may also be presented with the search results in a content itemslot of the search result webpage. Accordingly, the resource server 104and/or the client device 110 may request one or more content items fromthe content item selection system 108 to be presented in the contentitem slot of the search result webpage. The content item request mayinclude additional information, such as the client device information,the resource information, a quantity of content items, a format for thecontent items, the search query string, keywords of the search querystring, information related to the query (e.g., geographic locationinformation and/or temporal information), etc. In some implementations,a delineation may be made between the search results and the third-partycontent items to avert confusion.

In some implementations, the third-party content provider may manage theselection and serving of content items by content item selection system108. For example, the third-party content provider may set bid valuesand/or selection criteria via a user interface that may include one ormore content item conditions or constraints regarding the serving ofcontent items. A third-party content provider may specify that a contentitem and/or a set of content items should be selected and served forclient devices 110 having device identifiers associated with a certaingeographic location or region, a certain language, a certain operatingsystem, a certain web browser, etc. In another implementation, thethird-party content provider may specify that a content item or set ofcontent items should be selected and served when the resource, such as awebpage, document, etc., contains content that matches or is related tocertain keywords, phrases, etc. The third-party content provider may seta single bid value for several content items, set bid values for subsetsof content items, and/or set bid values for each content item. Thethird-party content provider may also set the types of bid values, suchas bids based on whether a user clicks on the third-party content item,whether a user performs a specific action based on the presentation ofthe third-party content item, whether the third-party content item isselected and served, and/or other types of bids.

While the foregoing has provided an overview of a system 100 forselecting and serving content items to client devices 110, examples ofcontent items served with resources will now be described in referenceto FIGS. 2-3. FIG. 2 depicts an example display 200 (shown in phantom)of a client device, such as client device 110 of FIG. 1, with a webbrowser 210 for displaying resources on the display 200. The web browser210 may operate by receiving input of a URL in an address bar, such as aweb address, from an input device (e.g., a pointing device, a keyboard,a touch screen, or another form of input device). In response, one ormore processors of a client device executing the instructions from theweb browser 210 may request data from another device connected to anetwork, such as network 106, referred to by the URL address (e.g., aresource server 104). The other device may then provide data to effectpresentation of the resource to the client device, which causes visualindicia to be displayed by the display 200 of the client device.Accordingly, the web browser 210 displays a retrieved resource 220, suchas a webpage.

An example resource 220 is shown displayed by the display 200 of theclient device using the web browser 210. The resource 220 includes afirst-party content portion 222, a first content item slot 224, and asecond content item slot 226. The first-party content portion 222includes the first-party content of the first-party content provider,such as a news article, a social network interface, an application, etc.In the example shown in FIG. 2, a first third-party content item may beselected and served in the first content item slot 224 and a secondthird-party content item may be selected and served in the secondcontent item slot 226, such as using content item selection system 108of FIG. 1.

In some implementations, the first-party content of the resource 220 mayimpact the impression effect decay for a content item presented with theresource 220. For example, if the first-party content of the resource220 is an application, such as a web game, then the presentation of athird-party content item in the first content item slot 224 and/or thesecond content item slot 226 may result in less of an impression effectand/or a more rapid impression effect decay. In another example, if thefirst-party content of the resource 220 is news article, then thepresentation of a third-party content item in the first content itemslot 224 and/or the second content item slot 226 may result in a greaterimpression effect and/or a slower impression effect decay. Suchfirst-party content characterizations may be categorized (e.g.,news-related resource content, application-related resource content,etc.) and represented by a corresponding value for a parameter (e.g., avalue of 1 for the parameter ResConType corresponds to a news-relatedresource content, 2 for application-related resource content, etc.).

According to various implementations, data for the content of theresource 220 may be sent to the content item selection system 108 ofFIG. 1. The content of the resource 220 may be parsed for keyword termsto determine the category of the content of the resource 220. Forexample, the content item selection system 108 may receive or extractkeyword terms and determine a category for the resource 220 based on thekeyword terms. In general, a category may be a set of words that conveythe same or similar ideas. A word category may be a set of synonyms,according to one implementation. For example, the text of the resource220 may include the word “hotel.” A word category that includes the word“hotel” may be as follows: category_1={inn, hotel, hostel, lodge, motel,public house, spa}

Such a category may be used to identify resources 220 devoted to thesame topic, but use different terminology to do so.

In various implementations, the category of the content of the resource220 may be determined based on the parsed keyword terms. For example, aresource 220 containing keyword terms for news may indicate a newsarticle. In other implementations, the category of the content of theresource 220 may be determined based on a structure of the resource 220.For example, the resource 220 having a first-party content portion 222having a long vertical textual portion may correspond to an articlecategory for the content of the resource 220. In other instances, afirst-party content portion 222 having several images may indicate andphoto album category for the content of the resource 220. In still otherinstances, a first-party content portion 222 having a Flash® module maycorrespond to an application category for the content of the resource220.

In another example, the publisher of content of the resource 220 mayimpact the impression effect decay for a content item presented with theresource 220. For example, if the publisher of the first-party contentdisplayed in the first-party content portion 222 is a first entity, suchas a well-known news publisher having readers that are more likely to beengaged with a third-party content item served with the first-partycontent, then the presentation of a third-party content item in thefirst content item slot 224 and/or the second content item slot 226 mayresult in a greater impression effect and/or a slower impression effectdecay. In another example, if the publisher of the first-party contentdisplayed in the first-party content portion 222 is a second entity,such as a social link aggregation publisher having readers that are lesslikely to be engaged with a third-party content item served with thefirst-party content, then the presentation of a third-party content itemin the first content item slot 224 and/or the second content item slot226 may result in less of an impression effect and/or a more rapidimpression effect decay. Of course other parameters associated with theresource 220 and/or the publisher of the resource 220 may be used.

FIG. 3 depicts a mobile client device 300, such as a smartphone ortablet, on which a resource 310 may be displayed by a display 302 of theclient device 300. In the example depicted in FIG. 3, the resource 310is a mobile application executing on the client device 300. In someimplementations, the mobile application resource 310 may execute code toeffect presentation of first-party content 312 (e.g., a mobile gameapplication), on the display 302 of the client device 300. In someimplementations, the resource 310 may also include code to request oneor more third-party content items 314 to be presented with thefirst-party content 312. In response, one or more processors of theclient device 300 executing the instructions may request data fromanother device (e.g., a content item selection system 108) connected toa network, such as network 106. The other device may then provide datato effect presentation of the third-party content item 314 to the clientdevice 300, which causes visual indicia to be displayed by the display302 of the client device 300.

In some implementations, the first-party content 312 may impact theimpression effect decay for a content item 314 presented with thefirst-party content 312. In the example shown in FIG. 3, if the resource310 includes first-party content 312 for a mobile game application, thenthe presentation of the third-party content item 314 may result in lessof an impression effect and/or a more rapid impression effect decay. Inanother example, if the resource 310 includes first-party content 312for a news application, then the presentation of the third-party contentitem 314 may result in a greater impression effect and/or a slowerimpression effect decay.

In another example, the publisher of the resource 310 may impact theimpression effect decay for a content item 314 presented with thefirst-party content 310. For example, if the publisher of the resource310 is a first entity, such as a mobile game application producer havingapplication users that are more likely to be engaged with a third-partycontent item 314 served with the first-party content 312, then thepresentation of a third-party content item 314 may result in a greaterimpression effect and/or a slower impression effect decay. In anotherexample, if the publisher of the resource 310 is a second entity, suchas a news aggregation application publisher having readers that are lesslikely to be engaged with a third-party content item 314 served with thefirst-party content 312, then the presentation of a third-party contentitem 314 may result in less of an impression effect and/or a more rapidimpression effect decay. Of course other parameters associated with theresource 310 and/or the publisher of the resource 310 may be used.

FIGS. 4-5 depict graphical diagrams depicting impression effects 400,500 decaying over a time period. In the example shown in FIG. 4, theimpression effect 400 lasts for a first period of time before slowlydecaying, then rapidly decays over a second period of time, followed bya slower decay until the impression effect 400 is substantially zero.Such an impression effect 400 curve may, for example, correspond to theimpression effect of a first content item presented to a client devicehaving a first aggregate profile and with a news article resource 220 ofFIG. 2 or a news aggregation application resource 310 of FIG. 3. In theexample shown in FIG. 5, the impression effect 500 rapidly decays over afirst period of time followed by a slower decay until the impressioneffect 500 is substantially zero. Such an impression effect 500 curvemay, for example, correspond to the impression effect of the same firstcontent item presented to the same client device having the same firstaggregate profile and with a web game application resource 220 of FIG. 2or a mobile game application resource 310 of FIG. 3. Other impressioneffect curves may be determined or generated based on other parametersdescribed herein.

The impression effect and decay thereof may be useful in determiningwhether to select and serve a previously served content item or anothercontent item. For example, if a relevant content item has been recentlyshown to a user of a client device and a slightly less relevant contentitem has not been recently shown, then it may be useful to use such databy the content item selection system 108 of FIG. 1 to select and servethe less relevant content item instead of reserving the recently servedcontent item. FIG. 6 is a block diagram of an example portion of thecontent item selection system 108 of FIG. 1 that utilizes the impressioneffect and decay in the selection and serving of content items. Thecontent item selection system 108 includes a content item selectionmodule 150 and one or more databases, such as a historical database 160,a content item database 170, and/or a predictive model database 180.

The content item selection module 150 is configured to receive a contentitem request 602 via the network 106. A client device, such as clientdevice 110 of FIG. 1, or a resource server, such as resource server 104,may send the content item request 602 to the content item selectionsystem 108 via the network 106. The content item request 602 may includeone or more current impression effect parameters representative ofcharacteristics of the client device (e.g., a unique identifierassociated with the client device, a type of client device, a displaytype of a client device, dimensions of the display, etc.), a currentclient device context, a client device intention (e.g., an interest inpurchasing a product, an intention to view more information on a topic,etc.), an aggregate profile (e.g., an aggregate profile of severalclient devices associated with the current client device), and/orcharacteristics of a resource with which the content item is to bepresented (e.g., a URL of the resource, one or more keywords of thecontent of the resource, text of the content of the resource, a title ofthe resource, a category of the resource, a type of the resource, aproperty of the resource, an interactivity level of the resource, aranking of the resource, a popularity of the resource, a category of apublisher associated with the resource, a type of a publisher associatedwith the resource, a property of a publisher associated with theresource, etc.). In some implementations, the foregoing currentimpression effect parameters may be appended to or included in a contentitem request URL (e.g., /page/contentitem?devid=abc123&devnfo=A34r0).

Responsive to the content item request 602, the content item selectionmodule 150 is configured to select and serve a content item 604. In someimplementations, the content item selection module 150 is configured toperform an auction. That is, the content item selection module 150 maygenerate one or more values for one or more content items based, atleast in part, on the content item request 602, and select one or morecontent items to be served. In some instances, the content itemselection module 150 ranks the values (e.g., highest to lowest) andselects the content item associated with a value based on the ranking(e.g., selecting the content item associated with the highest rankedvalue). As will be described in greater detail herein, a predictivemodel, such as an impression effect decay predictive model, may be usedby a predictive model module 152 of the content item selection module150 that modifies the value generated by the content item selectionmodule 150.

Data to effect presentation of the selected content item 604 may betransmitted or served by the content item selection module 150 to theclient device and/or the resource server via the network 106. The datacan include graphical data, textual data, image data, audio data, videodata, etc. that may be accessed from a database, such as the contentitem database 170 described herein.

The databases 160, 170, 180 shown in FIG. 6 may store data for and/orprovide data to the content item selection module 150. The databases160, 170, 180 may include a static storage device, such as ROM, solidstate drive (SSD), flash memory (e.g., EEPROM, EPROM, etc.), magneticdisc, optical disc, etc., a plurality of static storage devices, a cloudstorage system, a server, and/or any other electronic device capable ofstoring and providing data. While the implementation shown in FIG. 6depicts the databases 160, 170, 180 as separate databases, it should beunderstood that the databases 160, 170, 180 may be combined into asingle database or sets of databases.

The data stored in the historical database 160 may include data for oneor more prior impression effect parameters. The prior impression effectparameters may be associated with a client device, a content item, athird-party content provider, a resource, and/or a publisher. In someimplementations, the prior impression effect parameters may beassociated with a historical engagement of one or more client deviceswith one or more content items. That is, the historical database 160 maystore in a data structure, such as a data table, one or more priorimpression effect parameters about the context of a prior selection,serving, and/or interaction of one or more client devices with one ormore content items.

Such prior impression effect parameters may include whether a clientdevice performed a conversion action, such as clicking on one or morecontent items, purchasing a product associated with the one or morecontent items, registering for a service associated with the one or morecontent items, signing up for an e-mail list associated with the one ormore content items, etc.

In addition, the prior impression effect parameters may include priordevice information (e.g., one or more other previous resource requestsfrom the client device, one or more previous other content itemsreceived by the client device, previous contextual data for the clientdevice, previous intention data for the client device, one or moreprevious interests for the client device, an aggregate profile, etc.)and prior resource information (e.g., prior URLs of requested resources,one or more keywords of the content of the prior requested resources,text of the content of the prior resources, a title of the priorresources, a category of the prior resources, a type of the priorresources, a property of the prior resources, an interactivity level ofthe prior resources, a ranking of the prior resources, a popularity ofthe prior resources, a category of a publisher associated with the priorresources, a type of a publisher associated with the prior resources, aproperty of a publisher associated with the prior resources, etc.).

The prior impression effect parameters may also include prior contentitem parameters, such as a property of the prior content item, aninteractivity of the prior content item, a popularity of the prioritycontent item, a category of the prior content item, one or more keywordsassociated with the prior content item, a format of the prior contentitem, a group associated with the prior content item, a campaignassociated with the prior content item, etc.

The prior impression effect parameters may still further includetemporal parameters associated with the one or more client devices andthe one or more content items, such as a prior time of a day associatedwith when the one or more client devices were served the one or morecontent items (e.g., an exact time of the day, an hour of the day, acategorical period, such as morning, of the day, etc.), a prior day of aweek associated with when the one or more client devices were served theone or more content items (e.g., a Monday, a Tuesday, etc.), a prior dayof a month associated with when the one or more client devices wereserved the one or more content items, a prior day of a year associatedwith when the one or more client devices were served the one or morecontent items, a time differential between when the one or more clientdevices were served a content item and a conversion action performed bythe client device associated with the content item (e.g., five daysbetween when a content item is served and a conversion action), a numberof impressions of one or more content items before a conversion actionperformed by the client device associated with the content item (e.g., acontent item was shown three times before a conversion action), a numberof impressions of one or more content items associated with a clientdevice within a prior predetermined period of time (e.g., a number ofimpressions of a content item for the past 30 days, 60 days, 90 days,etc.), etc.

In some implementation, the prior impression effect parameters receivedwith the content item request 602 may be stored in the historicaldatabase 160. If a conversion action is performed in response to theselected and served content item 604, the client device may provide data606 indicative of such a conversion action. In some instances, theprovided data 606 may include a unique identifier such that the dataindicative of the conversion action may be associated with the storedparameters received with the content item request 602. Thus, theparameters of the content item request 602 may be used as priorimpression effect parameters to generate and/or update an impressioneffect decay predictive model.

The data stored in the content item database 170 may include data toeffect presentation of one or more content items. The data can includegraphical data, textual data, image data, audio data, video data, etc.The data stored in the content item database 170 may include uniqueidentifiers associated with the data such that the content itemselection module 150 can access the corresponding data based on theunique identifier.

In some implementations, the predictive model database 180 may storedata for one or more generated impression effect decay predictivemodels. For example, in some implementations an impression effect decaypredictive model may be generated for each content item and stored inthe predictive model database 180. In other implementations, animpression effect decay predictive model may be generated for eachclient device and stored in the predictive model database 180. Infurther implementations, an impression effect decay predictive model maybe generated for third-party content provider and/or for each campaignof the third-party content provider and stored in the predictive modeldatabase 180. In yet other implementations, other predictive models mayincorporate a impression effect decay predictive model, such as apredictive model for pCTR that incorporates the impression effect decay,a predictive model for pCVR that incorporates the impression effectdecay, etc. The predictive models described herein may each beassociated with a unique identifier such that the content item selectionmodule 150 can access the corresponding predictive model based on theunique identifier.

The content item selection module 150 includes a predictive model module152. In some implementations, the predictive model module 152 isconfigured to access data stored in the historical database, such as theone or more prior impression effect parameters discussed herein, and togenerate and/or update one or more predictive models described herein.As noted above, the generated and/or updated predictive models may bestored in the predictive model database 180. In some implementations,the predictive model module 152 may generate and/or update thepredictive model using a machine learning algorithm, such as aregression learning algorithm. Examples of such regression learningalgorithms include perceptron linear learning, maximum entropy logisticregression, support vector machine (SVM) regression with maximum entropygradient, descent least-squares stochastic gradient, etc.

The predictive model module 152 is also configured to access the one ormore current impression effect parameters from a data structure, such asthe content item request 602. In some implementations, the content itemselection module 150 receives the content item request 602 and may parseone or more current impression effect parameters included with thecontent item request 602. If the one or more current impression effectparameters are encoded for the content item request 602, the contentitem selection module 150 may decode the encoded current impressioneffect parameters. The predictive model module 152 accesses the parsedone or more current impression effect parameters.

The predictive model module 152 may also access one or more currentimpression effect parameters associated with a content item and/orthird-party content provider. For example, each content item that is tobe included in an auction performed by the content item selection module150 may have one or more parameters that may affect the impressioneffect and/or the decay thereof. For example, such parameters mayinclude a category of the content item, one or more keywords associatedwith the content item, a format of the content item, a group of thecontent item, a campaign of the content item, an interactivity level ofthe content item, a popularity of the content item, a category of athird-party content provider associated with the content item, a type ofa third-party content provider associated with the content item, aproperty of a third-party content provider associated with the contentitem, etc.

In some implementations, the predictive model module 152 may also accessa predictive model from the predictive model database 180 with which touse the accessed one or more current impression effect parameters. Insome implementations, a single predictive model may be accessed to beused with the one or more parameters to generate a value for eachcontent item to be in the auction performed by the content itemselection module 150. That is, the predictive model module 152 may usethe single predictive model with the received one or more parameters ofthe content item request 602 and one or more parameters of each contentitem in the auction to output a corresponding value for each contentitem.

In some implementations, a predictive model may be accessed from thepredictive model database 180 corresponding to a content item, athird-party content provider, a campaign, a resource, a publisher, or aclient device (for example, a predictive model may be associated with aclient device such that the same predictive model may be used for eachcontent item request 602 from that client device).

The predictive model module 152 outputs a value using a predictivemodel, such as the impression effect decay predictive models describedherein, and based on the one or more parameter inputs. In someimplementations, the outputted value may be a decay factor. The decayfactor may have a numerical value between 0, inclusive, and 1,inclusive, such as 0.647, that may be used to modify a value generatedby the content item selection module 150. In some instances, the decayfactor may be a discount factor that may be multiplied by the valuegenerated by the content item selection module 150. For example, a decayfactor of 0, which may correspond to when a content item is initiallyshown, may result in the value generated by the content item selectionmodule 150 to also be 0. Thus, a content item that has just beenselected and served to a client device is unlikely to be shown based onthe substantial reduction of the value by the decay factor. In anotherexample, a decay factor of 0.7, which may correspond to a content itembeing shown a few days ago, may result in the value generated by thecontent item selection module 150 to be reduced, but may still be likelyto be selected. A decay factor of 1, which may correspond to a contentitem being shown a few weeks ago, may not modify the value generated bythe content item selection module 150. That is, the decay factorindicates the content item has been shown a substantially long time agoand will not lessen the likelihood of the content item being selectedand served to the client device. In some implementations the valuegenerated by the content item selection module 150 may be divided by aninverse of the decay factor to increase the value generated by thecontent item selection module 150, thereby promoting content items thathave not been shown for a long period of time. In still otherimplementations, the value output by the predictive model may be addedor subtracted from the value generated by the content item selectionmodule 150.

In some other implementations, the outputted value may be anotherpredicted value, such as a value for pCTR or pCVR representative of aprobability that the content item will be clicked on or that aconversion will occur, respectively. As described in greater detailherein, the predictive model may integrate an impression effect decaypredictive model into the predictive model for the predicted value(e.g., integrated into a pCTR predictive model or a pCVR predictivemodel).

FIGS. 7-10 depict processes 700, 800, 900, 1000 that may be implementedby the content item selection system 108 to select and serve contentitems utilizing predictive models that factor in the impression effectand decay thereof. FIG. 7 depicts an implementation of a process 700that may be used by the predictive model module 152 for generating animpression effect decay predictive model based on prior impressioneffect parameters. The process 700 includes accessing data indicative ofprior impression effect parameters stored in a data structure (block702). The prior impression effect parameters may be accessed by thepredictive model module 152 of the content item selection system 108from a data structure, such as a data table, stored in a database, suchas the historical database 160. The prior impression effect parametersinclude known input parameters that are associated with known outputvalues. Examples of such known output values may include whether acontent item was clicked on, whether a conversion action occurred, etc.Such prior impression effect input parameters may include, for example,a number of impressions for one or more content items associated withthe known output value within a prior predetermined period of time, oneor more temporal parameters associated the one or more content itemsassociated with the known output value, one or more content itemparameters associated with the one or more content items associated withthe known output value, one or more prior third-party content providerparameters associated with the one or more content items associated withthe known output value, one or more prior device parameters associatedwith the one or more content items associated with the known outputvalue, one or more prior resource parameters associated with the one ormore content items associated with the known output value, etc.

A predictive model is generated based on the accessed prior impressioneffect parameters and the known output values (block 704). Thepredictive model module 152 may generate the impression effect decaypredictive model using a machine learning algorithm, such as aregression learning algorithm. Examples of such regression learningalgorithms include perceptron linear learning, maximum entropy logisticregression, support vector machine (SVM) regression with maximum entropygradient, descent least-squares stochastic gradient, etc. In someimplementations, an impression effect decay predictive model may begenerated for each content item, third-party content provider, campaign,resource, publisher, and/or client device.

The predictive model generated by the predictive model module 152 may beconfigured to model the impression effect decay over a predeterminedperiod of time. In some implementations, the predictive model module 152may be configured to generate predictive models for impression effectdecay of a period of time that is greater than one day. For example theperiod of time may be one week, two weeks, three weeks, four weeks, twomonths, three months, six months, one year, etc. In someimplementations, the period of time may be used to limit the priorimpression effect parameters used by the predictive model module 152 ingenerating the predictive model. That is, the period of time for whichthe predictive model is to be configured for may be used as a date spanfor the impression effect parameters. For example, if the predictivemodel is to be configured to model the impression effect over a periodof four weeks, then only the impression effect parameters associatedwith a known output value for a content item that occurred within fourweeks of the initial impression of the content item are used by thepredictive model module 152 in generating the predictive model.Impression effect parameters associated with a known output value for acontent item that occurred more than four weeks from the initialimpression of the content item may be omitted. In other implementations,all impression effect parameters associated with a known output valuefor a content item may be used to generate the predictive model.

In some implementations, the generated predictive model or models may bestored in a database, such as the predictive model database 180, suchthat the predictive model or models may be used by the predictive modelmodule 152 in response to another content item request. In someimplementations, each predictive model may include a unique identifier.In some implementations, the process 700 may receive additional priorimpression effect input parameters (block 702) and may update thepredictive model or regenerate the predictive model (block 704) based onthe additional prior impression effect input parameters and knownadditional output values.

FIG. 8 depicts an implementation of a process 800 for selecting andserving a content item using an impression effect decay predictivemodel. The process 800 includes accessing impression effect parametersstored in a data structure (block 802). The impression effect parametersmay include current impression effect parameters as well as priorimpression effect parameters. The current impression effect parametersmay be accessed by the predictive model module 152 from a datastructure, such as a memory, once the current impression effectparameters are parsed from a content item request, such as content itemrequest 602. The current impression effect parameters from the contentitem request 602 may include characteristics of the client device (e.g.,a unique identifier associated with the client device, a type of clientdevice, a display type of a client device, dimensions of the display), acurrent client device context, a client device intention, an aggregateprofile (e.g., an aggregate profile of several client devices associatedwith the current client device), and/or characteristics of a resourcewith which the content item is to be presented (e.g., a URL of theresource, one or more keywords of the content of the resource, text ofthe content of the resource, a title of the resource, a category of theresource, a type of the resource, a property of the resource, aninteractivity level of the resource, a ranking of the resource, apopularity of the resource, a category of a publisher associated withthe resource, a type of a publisher associated with the resource, aproperty of a publisher associated with the resource, etc.).

The current impression effect parameters may also be accessed from adata structure associated with a content item for which a value is to beoutputted from the predictive model. For example, each third-partycontent item may be associated with a data structure including contentitem parameters and the third-party content provider parameters. Suchparameters may include a category of the content item, one or morekeywords associated with the content item, a format of the content item,a group of the content item, a campaign of the content item, aninteractivity level of the content item, a popularity of the contentitem, a category of a third-party content provider associated with thecontent item, a type of a third-party content provider associated withthe content item, a property of a third-party content providerassociated with the content item, etc.

In some implementations, the prior impression effect parameters may beaccessed by the predictive model module 152 of the content itemselection system 108 from a data structure, such as a data table, storedin a database, such as the historical database 160. The prior impressioneffect parameters include those parameters associated with the clientdevice which is to be served a content item in response to a contentitem request. That is the prior impression effect parameters may beassociated with a historical engagement of the client device with one ormore content items.

The prior impression effect parameters may include parameters associatedwith prior content items, prior third-party content providers, priorresources, and/or prior publishers. Such prior impression effectparameters may include whether a client device performed a conversionaction, prior device parameters (e.g., one or more other previousresource requests from the client device, one or more previous othercontent items received by the client device, previous contextual datafor the client device, previous intention data for the client device,one or more previous interests for the client device, an aggregateprofile, etc.), prior resource parameters (e.g., prior URLs of requestedresources, one or more keywords of the content of the prior requestedresources, text of the content of the prior resources, a title of theprior resources, a category of the prior resources, a type of the priorresources, a property of the prior resources, an interactivity level ofthe prior resources, a ranking of the prior resources, a popularity ofthe prior resources, a category of a publisher associated with the priorresources, a type of a publisher associated with the prior resources, aproperty of a publisher associated with the prior resources, etc.),prior content item parameters (e.g., a property of the prior contentitem, an interactivity of the prior content item, a popularity of thepriority content item, a category of the prior content item, one or morekeywords associated with the prior content item, a format of the priorcontent item, a group associated with the prior content item, a campaignassociated with the prior content item, etc.), and/or temporalparameters associated with the client device (e.g., a prior time of aday associated with when the client device was served one or morecontent items, a prior day of a week associated with when the clientdevice was served one or more content items, a prior day of a monthassociated with when the client device was served one or more contentitems, a prior day of a year associated with when the client device wasserved one or more content items, a time differential between when theclient device was served a content item and a conversion actionperformed by the client device, a number of impressions of one or morecontent items before a conversion action was performed by the clientdevice, a number of impressions of one or more content items for theclient device within a prior predetermined period of time, etc.).

Using the impression effect parameters and an impression effect decaypredictive model, the predictive model module 152 determines a decayfactor for a content item (step 804). In some implementations, thepredictive model module 152 accesses an impression effect decaypredictive model from the predictive model database 180 with which touse the accessed impression effect parameters. In some implementations,a single impression effect decay predictive model may be accessed to beused with the parameters to generate a decay factor for each contentitem in an auction performed by the content item selection module 150.That is, the predictive model module 152 may use the single impressioneffect decay predictive model with the accessed parameters of thecontent item request 602 and the parameters of each content item in theauction to output a corresponding decay factor value for each contentitem. In other implementations, an impression effect decay predictivemodel may be accessed from the predictive model database 180corresponding to a content item, a third-party content provider, acampaign, a resource, a publisher, or a client device. In the example ofprocess 800 of FIG. 8, the decay factor is a numerical value indicativeof a predicted decay of a previous impression of the content item basedon the impression effect parameters. In some implementations, the decayvalue may be a numerical value between 0, inclusive, and 1, inclusive.

A value is determined for the content item based, at least in part, onthe decay factor and a bid associated with the content item (block 806).In an example, the content item selection module 150 determines thevalue using the decay factor output from the predictive model module152, a bid associated with the content item for which the decay factorwas generated, and another predicted value, such as a pCTR value or pCVRvalue. That is, the content item selection module 150 may generate ascore for the content item to be used in the auction based on the decayfactor, the bid value, and the predicted value or another modifyingvalue. In some instances, the decay factor may be a discount factor toreduce a score determined based on the bid and the predicted value bythe content item selection module 150. Such a score may be calculated byScore=D _(f) *B*Pwhere D_(f) is the decay factor, B is the bid value, and P is thepredicted value, such as pCTR or pCVR. In other implementations, thedecay factor may be divided, subtracted, or added to a score determinedbased on the bid and the predicted value by the content item selectionmodule 150.

The content item is selected based on the determined value (block 808).The content item may be selected by the content item selection module150 based on an auction where scores are determined for each contentitem in the auction. The content item selection module 150 may selectthe content item associated with the highest determined score after thedecay factor is factored in. In some implementations, each score isassociated with a unique identifier for the associated content item.Based on the determined values or score, the content item selectionmodule 150 may use the unique identifier associated with the highestdetermined value or score, in one implementation, to identify theselected content item associated with the highest determined value orscore.

Data to effect presentation of the selected content item (block 810) isprovided by the content item selection system 108 to a client deviceand/or a resource server via the network 106. In some implementations,the content item selection module 150 may access the data to effectpresentation of the selected content item from the content item database170. The data to effect presentation of the selected content item may beaccessed from the content item database 170 using the unique identifierassociated with the selected content item. The data to effectpresentation of the selected content item can include graphical data,textual data, image data, audio data, video data, etc. The client devicereceives the data to effect presentation of the selected content itemand may display the selected content item on a display of the clientdevice, such as those shown and described in reference to FIGS. 2-3.

In some implementations, the predictive model module 152 may generate animpression decay predictive model prior to or during the process ofselecting and serving a third-party content item to a client device. Forexample, FIG. 9 depicts an implementation of a process 900 in which thepredictive model module 152 generates an impression effect decaypredictive model, the generated impression effect decay predictive modelis used in the selection of a content item, the generated impressioneffect decay model is stored, data associated with the selected contentitem is received. In the example of FIG. 9, the process 900 includesaccessing prior impression effect parameters stored in a data structure(block 902). The accessing of the prior impression effect parameters maybe performed in a substantially similar manner to that described inreference to block 702 of FIG. 7. The prior impression effect parametersmay include parameters associated with prior content items, priorthird-party content providers, prior resources, and/or prior publishers.Such prior impression effect parameters may include whether a clientdevice performed a conversion action, prior device parameters, priorresource parameters, prior content item parameters, and/or temporalparameters associated with the client device as described herein.

An impression effect decay predictive model is generated by thepredictive model module 152 based, at least in part, on the priorimpression effect parameters (block 904). The generation of theimpression effect decay predictive model may be performed in asubstantially similar manner to that described in reference to block 704of FIG. 7. The predictive model module 152 may generate the impressioneffect decay predictive model using a machine learning algorithm, suchas a regression learning algorithm. Examples of such regression learningalgorithms include perceptron linear learning, maximum entropy logisticregression, support vector machine (SVM) regression with maximum entropygradient, descent least-squares stochastic gradient, etc. In someimplementations, an impression effect decay predictive model may begenerated for each content item, third-party content provider, campaign,resource, publisher, and/or client device.

Current impression effect parameters are received (block 906). Thecurrent impression effect parameters may be included in a content itemrequest, such as content item request 602, received by the content itemselection system 108. The current impression effect parameters may beparsed from the content item request to be used by the predictive modelmodule 152. The current impression effect parameters from the contentitem request may include characteristics of the client device, a currentclient device context, a client device intention, an aggregate profile,and/or characteristics of a resource with which the content item is tobe presented.

The current impression effect parameters may also be received for eachcontent item included in an auction to be performed by the content itemselection module 150. For example, each third-party content item may beassociated with content item parameters and the third-party contentprovider parameters. Such parameters may include a category of thecontent item, one or more keywords associated with the content item, aformat of the content item, a group of the content item, a campaign ofthe content item, an interactivity level of the content item, apopularity of the content item, a category of a third-party contentprovider associated with the content item, a type of a third-partycontent provider associated with the content item, a property of athird-party content provider associated with the content item, etc.

Using the current impression effect parameters and the generatedimpression effect decay predictive model, the predictive model module152 determines a decay factor for a content item (step 908). Thedetermination of the decay factor may be performed in a substantiallysimilar manner to that described in reference to block 804 of FIG. 8. Avalue is determined for the content item based, at least in part, on thedecay factor and a bid associated with the content item (block 910). Thedetermination of the value may be performed in a substantially similarmanner to that described in reference to block 806 of FIG. 8. Thecontent item is selected based on the determined value (block 912). Theselection of the content item may be performed in a substantiallysimilar manner to that described in reference to block 808 of FIG. 8.Data to effect presentation of the selected content item (block 914) isprovided by the content item selection system 108 to a client deviceand/or a resource server via the network 106. The providing of data toeffect presentation of the selected content item may be performed in asubstantially similar manner to that described in reference to block 810of FIG. 8.

In some implementations, the generated impression effect decaypredictive model may be stored (block 916). The generated impressioneffect decay predictive model may be stored in the predictive modeldatabase 180 with a unique identifier such that the predictive model maybe identified and reused for a subsequent content item request. Forexample, if an impression effect predictive model is generated for eachcontent item, third-party content provider, campaign, resource,publisher, and/or client device, then the stored predictive model may beaccessed when the same content item, third-party content provider,campaign, resource, publisher, and/or client device is involved.

Data may be received by the content item selection system 108 that isassociated with the selected content item (block 918). The data mayinclude data indicative of a click on the selected content item, dataindicative of a conversion action associated with the selected contentitem, data indicative of a viewing of the selected content item, etc.For example, a click on the selected content item by a user of a clientdevice may cause the client device to provide data indicative of theclick through the content item to the content item selection system 108.Such data may include a unique identifier associated with the contentitem, the client device, and/or the content item request that resultedin the selection and serving of the content item. The content itemselection system 108 may store the data indicative of the click throughin the historical database 160. The data indicative of the click throughmay be associated with the impression effect parameters for the selectedcontent item such that the data of the historical database 160 adds theadditional now-prior impression effect parameters for updating andrefining the predictive model (shown by the return to block 902 inphantom). Thus, the impression effect decay predictive model may improveand “learn” the distribution of time after an initial impression that ittakes for a user to engage with a content item.

In some implementations, it may be preferable to incorporate theimpression effect decay into another predictive model. For example,instead of outputting a decay factor to modify the score resulting fromthe bid and the predicted value, such as pCTR or pCVR, the impressioneffect decay may be incorporated into the predictive model that outputsthe pCTR or pCVR value. That is, the pCTR predictive model or pCVRpredictive model may be generated to utilize the impression effect decayto modify the outputted pCTR or pCVR value. FIG. 10 depicts animplementation of a process 1000 for integrating the impression effectdecay into such existing predictive models. The process 1000 includesreceiving impression effect parameters and a predictive model (block1002). The receiving of impression effect parameters may be performed ina substantially similar manner to that described in reference to block802 of FIG. 8. The predictive model module 152 may receive a predictivemodel, such as a pCTR predictive model or a pCVR predictive model fromthe predictive model database 180 with which to use the receivedimpression effect parameters, amongst other parameters that may be usedwith the pCTR predictive model or pCVR predictive model. The pCTRpredictive model or pCVR predictive model may be generated by thepredictive model module 152 using prior impression effect parameters andother input parameters with click-through data and/or conversion data asthe known output values.

A predicted value for a content item is determined using the predictivemodel and the impression effect parameters (block 1004). The receivedimpression effect parameters are used as inputs to the predictive model,such as a pCTR or pCVR predictive model, to output a predicted value,such as a pCTR value or a pCVR value. The pCTR value is representativeof the probability that a user of the client device will click on thecontent item based on the impression effect parameters and any otherparameters for the pCTR predictive model. The pCVR value isrepresentative of the probability that a user of the client deviceperform a conversion action in response to serving a content item basedon the impression effect parameters and any other parameters for thepCVR predictive model.

A value for the content item is determined based, at least in part, onthe predicted value and a bid associated with the content item (block1006). The predicted value in the process of FIG. 10 incorporates in theimpression effect decay. Thus, the content item selection module 150 maysimply determine a value, such as a score, using the predicted valuefrom the predictive model module 152, such as a pCTR value or pCVR valuethat incorporates in the impression effect decay, and a bid associatedwith the content item.

The content item is selected based on the determined value (block 1008).The content item may be selected by the content item selection module150 based on an auction where scores are determined for each contentitem in the auction. The content item selection module 150 may selectthe content item associated with the highest determined value or score.In some implementations, each value or score is associated with a uniqueidentifier for the associated content item. Based on the determinedvalues, the content item selection module 150 may use the uniqueidentifier associated with the highest determined value or score, in oneimplementation, to identify the selected content item associated withthe highest determined value or score.

Data to effect presentation of the selected content item (block 1010) isprovided by the content item selection system 108 to a client deviceand/or a resource server via the network 106. In some implementations,the content item selection module 150 may access the data to effectpresentation of the selected content item from the content item database170. The data to effect presentation of the selected content item may beaccessed from the content item database 170 using the unique identifierassociated with the selected content item. The data to effectpresentation of the selected content item can include graphical data,textual data, image data, audio data, video data, etc. The client devicereceives the data to effect presentation of the selected content itemand may display the selected content item on a display of the clientdevice, such as those shown and described in reference to FIGS. 2-3.

In some implementations, a range of values for one or more impressioneffect parameters may be input into an impression effect decaypredictive model to output a range of values. For example, if animpression effect decay predictive model is generated for a specificcontent item of a third-party content provider, a range of values for asingle parameter, such as various aggregate profiles, categories ofresources, etc., may be input into the predictive model whilemaintaining other impression effect parameters constant such that therange of values are outputted. The range of outputted values may be usedto generate a graph, data table, or other data aggregation object for athird-party content provider to analyze how an impression effect decaysrelative to the selected parameter for the impression effect decaypredictive model. The third-party content provider may use suchoutputted values to optimize a content item campaign, resources withwhich content items are presented, etc.

FIG. 11 is a block diagram of a computer system 1100 that can be used toimplement the client device 110, content item selection system 108,third-party content server 102, resource server 104, etc. The computingsystem 1100 includes a bus 1105 or other communication component forcommunicating information and a processor 1110 or processing modulecoupled to the bus 1105 for processing information. The computing system1100 can also include one or more processors 1110 or processing modulescoupled to the bus for processing information. The computing system 1100also includes main memory 1115, such as a RAM or other dynamic storagedevice, coupled to the bus 1105 for storing information, andinstructions to be executed by the processor 1110. Main memory 1115 canalso be used for storing position information, temporary variables, orother intermediate information during execution of instructions by theprocessor 1110. The computing system 1100 may further include a ROM 1120or other static storage device coupled to the bus 1105 for storingstatic information and instructions for the processor 1110. A storagedevice 1125, such as a solid state device, magnetic disk or opticaldisk, is coupled to the bus 1105 for persistently storing informationand instructions. Computing device 1100 may include, but is not limitedto, digital computers, such as laptops, desktops, workstations, personaldigital assistants, servers, blade servers, mainframes, cellulartelephones, smart phones, mobile computing devices (e.g., a notepad,e-reader, etc.) etc.

The computing system 1100 may be coupled via the bus 1105 to a display1135, such as a Liquid Crystal Display (LCD), Thin-Film-Transistor LCD(TFT), an Organic Light Emitting Diode (OLED) display, LED display,Electronic Paper display, Plasma Display Panel (PDP), and/or otherdisplay, etc., for displaying information to a user. An input device1130, such as a keyboard including alphanumeric and other keys, may becoupled to the bus 1105 for communicating information and commandselections to the processor 1110. In another implementation, the inputdevice 1130 may be integrated with the display 1135, such as in a touchscreen display. The input device 1130 can include a cursor control, suchas a mouse, a trackball, or cursor direction keys, for communicatingdirection information and command selections to the processor 1110 andfor controlling cursor movement on the display 1135.

According to various implementations, the processes and/or methodsdescribed herein can be implemented by the computing system 1100 inresponse to the processor 1110 executing an arrangement of instructionscontained in main memory 1115. Such instructions can be read into mainmemory 1115 from another computer-readable medium, such as the storagedevice 1125. Execution of the arrangement of instructions contained inmain memory 1115 causes the computing system 1100 to perform theillustrative processes and/or method steps described herein. One or moreprocessors in a multi-processing arrangement may also be employed toexecute the instructions contained in main memory 1115. In alternativeimplementations, hard-wired circuitry may be used in place of or incombination with software instructions to effect illustrativeimplementations. Thus, implementations are not limited to any specificcombination of hardware circuitry and software.

Although an implementation of a computing system 1100 has been describedin FIG. 11, implementations of the subject matter and the functionaloperations described in this specification can be implemented in othertypes of digital electronic circuitry, or in computer software,firmware, or hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them.

Implementations of the subject matter and the operations described inthis specification can be implemented in digital electronic circuitry,or in computer software embodied on a tangible medium, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.The subject matter described in this specification can be implemented asone or more computer programs, i.e., one or more modules of computerprogram instructions, encoded on one or more computer storage media forexecution by, or to control the operation of, data processing apparatus.Alternatively or in addition, the program instructions can be encoded onan artificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate components or media (e.g., multiple CDs, disks, or otherstorage devices). Accordingly, the computer storage medium is bothtangible and non-transitory.

The operations described in this specification can be performed by adata processing apparatus on data stored on one or morecomputer-readable storage devices or received from other sources.

The terms “data processing apparatus,” “computing device,” “processingcircuit,” or “processing module” encompass all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, a portion of a programmed processor, or combinations of theforegoing. The apparatus can include special purpose logic circuitry,e.g., an FPGA or an ASIC. The apparatus can also include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them. The apparatus and execution environment canrealize various different computing model infrastructures, such as webservices, distributed computing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVDdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD monitor,for displaying information to the user and a keyboard and a pointingdevice, e.g., a mouse or a trackball, by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features specific to particularimplementations. Certain features described in this specification in thecontext of separate implementations can also be implemented incombination in a single implementation. Conversely, various featuresdescribed in the context of a single implementation can also beimplemented in multiple implementations separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated in a single software product or packagedinto multiple software products embodied on tangible media.

References to “or” may be construed as inclusive so that any termsdescribed using “or” may indicate any of a single, more than one, andall of the described terms.

Thus, particular implementations of the subject matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

The claims should not be read as limited to the described order orelements unless stated to that effect. It should be understood thatvarious changes in form and detail may be made by one of ordinary skillin the art without departing from the spirit and scope of the appendedclaims. All implementations that come within the spirit and scope of thefollowing claims and equivalents thereto are claimed.

What is claimed is:
 1. A method of selecting and serving a content item based on a decay of an impression effect for the content item, comprising: accessing, by a processing module, one or more impression effect parameters representative of characteristics of a client device, an impression effect parameter of the one or more impression effect parameters indicating a historical engagement by the client device with the content item and including a determination that the client device performed a conversion action with the content item, the one or more impression effect parameters stored in a data structure; determining, using a processing module, for the client device, a decay factor of the impression effect for the content item using an impression effect decay predictive model and using the impression effect parameter indicating the historical engagement by the client device with the content item with which the client device performed the conversion action; determining, using a processing module, a value for the content item based, at least in part, on the decay factor and a bid associated with the content item with which the client device performed the conversion action; selecting the content item with which the client device performed the conversion action based, at least in part, on the determined value for the content item with which the client device performed the conversion action; and providing data to effect presentation of the selected content item at the client device.
 2. The method of claim 1, wherein the impression effect decay predictive model is configured to model the impression effect decay over a predetermined period of time.
 3. The method of claim 1, wherein the value is a score based on the decay factor, the bid associated with the content item and a predicted value.
 4. The method of claim 3, wherein the predicted value is a pCTR value or a pCVR value.
 5. The method of claim 1, wherein the one or more impression effect parameters include one or more parameters associated with a current client device context.
 6. The method of claim 1, wherein the one or more impression effect parameters include one or more parameters associated with the content item.
 7. The method of claim 1, wherein the one or more impression effect parameters include one or more parameters associated with a resource with which the content item is to be presented.
 8. The method of claim 1, wherein the historical engagement includes one or more impressions of the content item associated with the client device within a prior predetermined period of time.
 9. The method of claim 1, wherein the one or more impression effect parameters include one or more parameters associated with an aggregate profile.
 10. The method of claim 1, wherein the impression effect decay predictive model is generated using a regression learning algorithm.
 11. The method of claim 1, wherein the characteristics of the client device include any of a type of client device, a display type of the client device, and a dimension of a display of the client device.
 12. A system for serving content items in content item slots comprising: one or more processing modules; and one or more storage devices storing instructions that, when executed by the one or more processing modules, cause the one or more processing modules to perform operations comprising: accessing prior impression effect parameters stored in a data structure, wherein the prior impression effect parameters are representative of characteristics of a client device and the prior impression parameters include an impression effect parameter indicating a historical engagement by the client device with a content item and a determination that the client device performed a conversion action with the content item, generating an impression effect decay predictive model based, at least in part, on the impression effect parameter indicating a historical engagement by the client device with the content item with which the client device performed the conversion action, receiving one or more current impression effect parameters, determining, for the client device, a decay factor of an impression effect for the content item using the generated impression effect decay predictive model and the impression effect parameter indicating the historical engagement by the client device with the content item with which the client device performed the conversion action, determining a value for the content item with which the client device performed the conversion action based, at least in part, on the decay factor and a bid associated with the content item with which the client device performed the conversion action, selecting the content item with which the client device performed the conversion action based, at least in part, on the determined value for the content item with which the client device performed the conversion action, and providing data to effect presentation of the selected content item at the client device.
 13. The system of claim 12, wherein the current impression effect parameters include one or more current parameters associated with a current client device context.
 14. The system of claim 12, wherein the historical engagement includes one or more prior impressions of the content item associated with the client device within a prior predetermined period of time.
 15. The system of claim 14, wherein the current impression effect parameters include one or more parameters associated with a resource with which the content item is to be presented and one or more parameters associated with the content item.
 16. The system of claim 12, wherein the instructions cause the one ore more processing modules to perform operations further comprising: storing the generated impression effect decay predictive model.
 17. A computer readable storage device storing instructions that, when executed by one or more processing modules, cause the one or more processing modules to perform operations comprising: accessing one or more impression effect parameters and a predictive model, each of the one or more impression effect parameters representative of characteristics of a client device and including a determination that the client device performed a conversion action with a content item, an impression effect parameter of the one or more impression effect parameters indicating a historical engagement by the client device with a content item with which the client device performed the conversion action; determining, for the client device, a predicted value for the content item using the predictive model and using the received one or more impression effect parameters representative of characteristics of the client device and the impression effect parameter indicating the historical engagement by the client device with the content item with which the client device performed the conversion action; determining a value for the content item with which the client device performed the conversion action based, at least in part, on the predicted value and a bid associated with the content item with which the client device performed the conversion action; selecting the content item with which the client device performed the conversion action based, at least in part, on the determined value for the content item; and providing data to effect presentation of the selected content item at the client device.
 18. The computer readable storage device of claim 17, wherein the predicted value is a pCTR value or a pCVR value and wherein the predictive model is generated using a regression learning algorithm based, at least in part, on a historical engagement of the client device with the content item.
 19. The computer readable storage device of claim 17, wherein the one or more accessed impression effect parameters include: one or more parameters associated with the content item, one or more current parameters associated with a current client device context, and one or more parameters associated with a resource with which the content item is to be presented. 